arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2008.11795cond-mat.mtrl-sci
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Al-Cu-Mg合金全浓度空间的精确深势模型

Accurate Deep Potential model for the Al-Cu-Mg alloy in the full concentration space

Wanrun Jiang, Yuzhi Zhang, Linfeng Zhang, Han Wang

摘要:

结合第一性原理的准确性和潜在的势能面(PES)的描述效率是哲学家的石头,通过原子模拟解开物质的性质。由于相关PES的复杂性和非线性性质,这对于多组分合金体系特别具有挑战性。在这项工作中,我们开发了一个精确的PES模型的铝-铜-镁系统采用深电位(DP),神经网络为基础的PES表示,和DP发生器(DP-GEN),一个并发学习方案,生成一组紧凑的从头算数据进行训练。由此产生的DP模型给出的预测符合第一性原理计算的各种二元和三元体系的基本能量和力学性质,包括形成能,平衡体积,状态方程,间隙能,空位和表面形成能,以及弹性模量。大量的基准测试表明,DP模型是准备好的,将是有用的原子模型的Al-Cu-Mg系统的浓度范围内的全。

英文摘要:

Combining first-principles accuracy and empirical-potential efficiency for the description of the potential energy surface (PES) is the philosopher's stone for unraveling the nature of matter via atomistic simulation. This has been particularly challenging for multi-component alloy systems due to the complex and non-linear nature of the associated PES. In this work, we develop an accurate PES model for the Al-Cu-Mg system by employing Deep Potential (DP), a neural network based representation of the PES, and DP Generator (DP-GEN), a concurrent-learning scheme that generates a compact set of ab initio data for training. The resulting DP model gives predictions consistent with first-principles calculations for various binary and ternary systems on their fundamental energetic and mechanical properties, including formation energy, equilibrium volume, equation of state, interstitial energy, vacancy and surface formation energy, as well as elastic moduli. Extensive benchmark shows that the DP model is ready and will be useful for atomistic modeling of the Al-Cu-Mg system within the full range of concentration.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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